Class 9 | Lab 8

Class 9 Lab | GIS Suitability using a Multi-Criteria Evaluation (MCE)

Class 9 Lab preamble:

• In both the lecture demo and in the Class 9 assignment, somewhat different suitability methods are utilized. In this MCE suitability analysis, vector features are first converted to raster themes and the raster calculator is utilized to deliver a non-weighted suitability.

• The goal of this lab suitability analysis is to located suitable development sites based on the following criteria:

  • Close to roads
  • Away from water bodies
  • Not in a protected region

• The location is state of Assam in India. The input data will come from OpenStreetMap. The layers included are as follows:

  • protected_regions
  • roads
  • water_polygons
  • water_polylines
  • boundary
  

  Suitability Inputs

• The data download is located as follows:

  • class 9 lab 8 data

Step 1:

• This MCE evaluation will be done in raster overlay. So first item of data preparation is to convert vector > raster via Processing Toolbox:

Rasterize Tool

• To start, input the roads layer and populate the tool with the following parameters:

  • 1 as the A fixed value to burn
  • Geoferenced units as the Output raster size units
  • 15 as both Width/Horizontal resolution and Height/Vertical resolution
  • Output extent = boundary (choose boundary at far right of dialog box)
  

  Tool Parameters

• Review Result:

Result

Note: if the tool produces polygon-like results that appear much different than the image above, re-run the tool.

• While this process can be repeated layer by layer, batch processing can speed up this process.

• To start, at the bottom of the tool following the roads_raster run, find Run as Batch Process. Click and proceed:

Batch Processing

• The tool is then populated with the remaining layers (but not roads as that is done). Make sure to populate with the same parameters as roads run. At the far right of the tool dialog, there will be a button for the output location. Select the top button and populate with the autofill mode set to fill with parameter values and input layer. This will result in a hash value unique to each input that now will be exported to the chosen export location. Keep in mind that once the tool finishes, the results will not be loaded into the layers panel; they will be placed in the export location (and empty exports folder already exists for this location in the lab data folder):

Batch Processing Parameters

• Load the raster results and check to make sure their boolean extent (value 1) equates the vector version of the feature layer:

Check Boolean Result

Step 2:

• Given that water as polyline vs polygons are not a meaningful distinction for this analysis, it makes sense to merge the water themes. Here Polygons and Polylines for water really represent the same theme - water assets. So those can be merged via Raster Calculator and set to output as raster_water_merged:

Raster Calculator

• Before proceeding, one issue needs to be resovled. Where the polylines and polygons intersect, a value of 2 is produced. This value should really be simply 1. Simply re-run Raster Calculator, selecting all values greater than 0 to produce the value of 1:

• "raster_water_merged@1" > 0

• Save the output as raster_water.tif

Raster Calculator

Step 3:

• In review of the original criteria we see that their are two distance criteria:

  • Close to roads
  • Away from water bodies

• Distance criteria is handled well by the Raster Distance GDAL tool. Here a pixel value is assigned to every cell location relative to the theme feature - road and water bodies, respectively. This resulting raster can be then used to determine suitable areas which are within certain distance from the input. Locate the tool:

Proximity - Raster Distance

• To start, input the roads_roads raster and populate the tool with the following parameters:

  • Georeferenced coordinates as the Distance units
  • 5000 (5 kilometers) as the Maximum distance
  • Nodata value to use for the destination proximity raster value is Not set
  • Output = roads_proximity.tif

Note: this process is memory intensive; expect completion to take upwards to 15 minutes to complete.

• In review of the result, overlay the roads polyline feature atop roads_proximity.tif. Change the Max value to 5000 in symbology for roads_proximity.tif so that cell values are scaled better. Here we can see how distance from roads is indeed the result of the tool:

Proximity Distance Result

Step 4:

• Repeat the proximity distance process of Step 3 with input of the raster_water layer, same parameters:

  • Georeferenced coordinates as the Distance units
  • 5000 (5 kilometers) as the Maximum distance
  • Nodata value to use for the destination proximity raster value is Not set
  • Output = water_proximity.tif

• In review of the result, overlay the water features atop water_proximity.tif. Change the Max value to 5000 in symbology for water_proximity.tif so that cell values are scaled better. Here we can see how distance from water is indeed the result of the tool:

Proximity Result

Step 5:

• With distance analysis complete, scores are applied based on thresholds. The following score schema will be used for roads_proximity.tif. This schema give more points to less distance (closer to roads) across 3 classes:

  • 0-1000m –> 100

  • 1000-5000m –> 50

  • 5000m –> 10

• This can be accomplished several ways. In the past, we’ve used a table to reclass a raster. Here we will use a raster calculator formula.

• The following formula will be utilized in Raster Calculator with roads_proximity.tif as input and an output named roads_reclass.tif:

  • 100*("roads_proximity@1"<=1000) + 50*("roads_proximity@1">1000)*("roads_proximity@1"<=5000) + 10*("roads_proximity@1">5000)

• In review of the result roads_reclass.tif , the raster can be symbolized to show the 3 score classes as distinct:

Raster Reclass Result

Step 6:

• Repeat the re-classification process for the water_proximity.tif. Here the scheme will be reverse, where pixels that are further away from water shall have higher score:

  • 0-1000m –> 10

  • 1000 -5000m —> 50

  • 5000m –> 100

• The following formula will be utilized in Raster Calculator with water_proximity.tif as input and an output named water_reclass.tif:

  • 100*("water_proximity@1">5000) + 50*("water_proximity@1">1000)*("water_proximity@1"<=5000) + 10*("water_proximity@1"<1000)

• In review of the result water_reclass.tif , the raster can be symbolized to show the 3 score classes as distinct:

Water Reclass Result

Note: here the white and light blue values are better than the dark blue which is distance < 1000m from waterbodies.

Step 7:

• The next and final analysis process will produce the final suitability result. In the following formula, the reclass rasters and the protected regions are the factors that are multiplied to produce suitability results within the state boundary. By multiplying the product at the end by raster_boundary@1, cells outside the state boundary are discarded.

  • Output name = overlay.tif

  • ("roads_reclass@1" + "water_reclass@1")*("raster_protected_regions@1" != 1 )*"raster_boundary@1"

Note: the formula will need to be altered to include the correct naming from the batch process hash numbers added to raster_protected_regions and raster_boundary.

Overlay Result

Step 8:

• Symbolization for the 0 - 200 suitable range via layer properties for overlay.tif

  • Select singleband_pseudocolor renderer and the Spectral color ramp.

Range Symbolization

Symbolization Result

• While the values were discarded outside the state boundary, zero values still exist to the raster extent edges. To visualize at boundary edge, an inverted polygon function can be applied.

• Place the original vector boundary at top of layers panel.

• Navigate to Properties > Symbology and change Single symbol to Inverted polygons and fill as white.

Invert Polygon for Visual Mask

Review the Final Suitability:

Final Suitability